UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
Call for Paper
Volume 9 | Issue 1 | January 2022

JETIREXPLORE- Search Thousands of research papers



WhatsApp Contact
Click Here

Published in:

Volume 6 Issue 6
June-2019
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

7.95 impact factor calculated by Google scholar

Unique Identifier

Published Paper ID:
JETIRDF06030


Registration ID:
223854

Page Number

151-156

Share This Article


Jetir RMS

Title

IDENTIFYING TWITTER BOTS BASED ON TRENDING HASHTAGS USING MACHINE LEARNING ALGORITHMS

Abstract

A bot is a piece of software that completes automated task over the internet .On social media the prevalence of bot is ubiquitous. Nearly 48 million twitter accounts are automated using bots. Detecting bots is necessary in order to identify bad actors in “Twitter verse” and protect genuine users from misinformation and malicious intents. According to a study released by Pew Research Center, these bots contribute to approximately 66% of total tweets in twitter.Bots are becoming smarter, they mimic humans to avoid being detected and suspended, and increase throughput by creating many accounts.The idea is to develop a bot detection algorithm to identify twitter bot accounts by using attributes like followers _ count, no of tweets or likes, status _ count etc. Thus evaluate these features using k-nearest neighbour algorithm and Random Forest. Finally the result of two models are combined using suitable ensemble method to produce more accurate solution. The Test dataset is prepared specifically for any hashtag. The dataset consists details of the accounts which uses that hashtag. This dataset is cleaned and used as a test dataset.

Key Words

Twitter Bot detection, Hashtag, KNN, Random Forest, Evaluation module.

Cite This Article

"IDENTIFYING TWITTER BOTS BASED ON TRENDING HASHTAGS USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.151-156, June 2019, Available :http://www.jetir.org/papers/JETIRDF06030.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"IDENTIFYING TWITTER BOTS BASED ON TRENDING HASHTAGS USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp151-156, June 2019, Available at : http://www.jetir.org/papers/JETIRDF06030.pdf

Publication Details

Published Paper ID: JETIRDF06030
Registration ID: 223854
Published In: Volume 6 | Issue 6 | Year June-2019
DOI (Digital Object Identifier):
Page No: 151-156
Country: Chennai, Tamil Nadu, India .
Area: Engineering
ISSN Number: 2349-5162


Preview This Article


Downlaod

Click here for Article Preview

Download PDF

Downloads

0002712

Print This Page

Current Call For Paper

Jetir RMS